"""simple docstring"""


import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow


if is_torch_available():
	import torch

	from transformers import XLMRobertaModel






@require_sentencepiece
@require_tokenizers
@require_torch
class   __UpperCAmelCase		(      unittest.TestCase      ):

				@slow
				def 						_a      ( self ):
										lowerCamelCase__   =XLMRobertaModel.from_pretrained("xlm-roberta-base" )
										lowerCamelCase__   =torch.tensor([[0, 581, 10269, 83, 99942, 136, 60742, 23, 70, 80583, 18276, 2]] )
										# The dog is cute and lives in the garden house

										lowerCamelCase__   =torch.Size((1, 12, 768) )  # batch_size, sequence_length, embedding_vector_dim
										lowerCamelCase__   =torch.tensor(
										    [[-0.0_1_0_1, 0.1_2_1_8, -0.0_8_0_3, 0.0_8_0_1, 0.1_3_2_7, 0.0_7_7_6, -0.1_2_1_5, 0.2_3_8_3, 0.3_3_3_8, 0.3_1_0_6, 0.0_3_0_0, 0.0_2_5_2]] )
										#  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
										#  xlmr.eval()
										#  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
										with torch.no_grad():
																lowerCamelCase__   =model(_lowerCamelCase )["last_hidden_state"].detach()
										self.assertEqual(output.shape      ,					_lowerCamelCase )
										# compare the actual values for a slice of last dim
										self.assertTrue(torch.allclose(output[:, :, -1]      ,					_lowerCamelCase      ,					atol=1E-3 ) )






				@slow
				def 						_a      ( self ):
										lowerCamelCase__   =XLMRobertaModel.from_pretrained("xlm-roberta-large" )
										lowerCamelCase__   =torch.tensor([[0, 581, 10269, 83, 99942, 136, 60742, 23, 70, 80583, 18276, 2]] )
										# The dog is cute and lives in the garden house

										lowerCamelCase__   =torch.Size((1, 12, 1024) )  # batch_size, sequence_length, embedding_vector_dim
										lowerCamelCase__   =torch.tensor(
										    [[-0.0_6_9_9, -0.0_3_1_8, 0.0_7_0_5, -0.1_2_4_1, 0.0_9_9_9, -0.0_5_2_0, 0.1_0_0_4, -0.1_8_3_8, -0.4_7_0_4, 0.1_4_3_7, 0.0_8_2_1, 0.0_1_2_6]] )
										#  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
										#  xlmr.eval()
										#  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
										with torch.no_grad():
																lowerCamelCase__   =model(_lowerCamelCase )["last_hidden_state"].detach()
										self.assertEqual(output.shape      ,					_lowerCamelCase )
										# compare the actual values for a slice of last dim
										self.assertTrue(torch.allclose(output[:, :, -1]      ,					_lowerCamelCase      ,					atol=1E-3 ) )



"""simple docstring"""


from __future__ import annotations

a				='#'






class   __UpperCAmelCase		:

				def __init__( self ):
										lowerCamelCase__   ={}

				def 						_a      ( self      ,					_lowerCamelCase ):
										lowerCamelCase__   =self._trie
										for char in text:
																if char not in trie:
																						lowerCamelCase__   ={}
																lowerCamelCase__   =trie[char]
										lowerCamelCase__   =True

				def 						_a      ( self      ,					_lowerCamelCase ):
										lowerCamelCase__   =self._trie
										for char in prefix:
																if char in trie:
																						lowerCamelCase__   =trie[char]
																else:
																						return []
										return self._elements(_lowerCamelCase )






				def 						_a      ( self      ,					_lowerCamelCase ):
										lowerCamelCase__   =[]
										for c, v in d.items():
																lowerCamelCase__   =[" "] if c == END else [(c + s) for s in self._elements(_lowerCamelCase )]
																result.extend(_lowerCamelCase )
										return tuple(_lowerCamelCase )





a				=Trie()
a				=('depart', 'detergent', 'daring', 'dog', 'deer', 'deal')
for word in words:
	trie.insert_word(word)





def      lowerCamelCase_					(   __lowerCAmelCase							)				->   tuple:




						'''simple docstring'''



						lowerCamelCase__   =trie.find_word(__lowerCAmelCase							)
						return tuple(string + word for word in suffixes							)





def      lowerCamelCase_					(   )				->   None:




						'''simple docstring'''



						print(autocomplete_using_trie("de"							)							)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()


